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Particle Swarm Optimization for Predicting the Development Effort of Software Projects

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  • Mariana Dayanara Alanis-Tamez

    (Centro de Investigación en Computación, Instituto Politécnico Nacional, Juan de Dios Bátiz s/n, Nueva Industrial Vallejo, GAM, CDMX, Mexico City 07700, Mexico
    Oracle, Fusion Adaptative Intelligence, Paseo Valle Real 1275, Valle Real, Zapopan, Jal, Guadalajara 45136, Mexico)

  • Cuauhtémoc López-Martín

    (Department of Information Systems, Universidad de Guadalajara, Periférico Norte N° 799, Núcleo Universitario Los Belenes, Zapopan 45100, Jalisco, Mexico)

  • Yenny Villuendas-Rey

    (Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional, Juan de Dios Bátiz s/n, Nueva Industrial Vallejo, GAM, CDMX, Mexico City 07700, Mexico)

Abstract

Software project planning includes as one of its main activities software development effort prediction (SDEP). Effort (measured in person-hours) is useful to budget and bidding the projects. It corresponds to one of the variables most predicted, actually, hundreds of studies on SDEP have been published. Therefore, we propose the application of the Particle Swarm Optimization (PSO) metaheuristic for optimizing the parameters of statistical regression equations (SRE) applied to SDEP. Our proposal incorporates two elements in PSO: the selection of the SDEP model, and the automatic adjustment of its parameters. The prediction accuracy of the SRE optimized through PSO ( PSO-SRE ) was compared to that of a SRE model. These models were trained and tested using eight data sets of new and enhancement software projects obtained from an international public repository of projects. Results based on statistically significance showed that the PSO-SRE was better than the SRE in six data sets at 99% of confidence, in one data set at 95%, and statistically equal than SRE in the remaining data set. We can conclude that the PSO can be used for optimizing SDEP equations taking into account the type of development, development platform, and programming language type of the projects.

Suggested Citation

  • Mariana Dayanara Alanis-Tamez & Cuauhtémoc López-Martín & Yenny Villuendas-Rey, 2020. "Particle Swarm Optimization for Predicting the Development Effort of Software Projects," Mathematics, MDPI, vol. 8(10), pages 1-21, October.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:10:p:1819-:d:430487
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    References listed on IDEAS

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    1. Huang, Sun-Jen & Chiu, Nan-Hsing & Chen, Li-Wei, 2008. "Integration of the grey relational analysis with genetic algorithm for software effort estimation," European Journal of Operational Research, Elsevier, vol. 188(3), pages 898-909, August.
    2. Anupama Kaushik & Devendra Kumar Tayal & Kalpana Yadav, 2020. "The Role of Neural Networks and Metaheuristics in Agile Software Development Effort Estimation," International Journal of Information Technology Project Management (IJITPM), IGI Global, vol. 11(2), pages 50-71, April.
    3. Arturo Chavoya & Cuauhtemoc Lopez-Martin & Irma R Andalon-Garcia & M E Meda-Campaña, 2012. "Genetic Programming as Alternative for Predicting Development Effort of Individual Software Projects," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-10, November.
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